In order to answer questions and analyze business information, end users traditionally have to initiate requests for information each time it is needed, scrub large sets of data, and filter this data in connection with each request. Companies have traditionally stored business data in local relational databases; however, companies are moving more and more of their data to the cloud. This results in many companies having critical business data in both local relational databases as well as in the cloud. Having data from multiple mixed data sources complicates retrieval times and creates difficulties in presenting the data to the user in a cohesive as well as efficient manner. The end user requires a fast and flexible run time solution to automatically connect, choose, and analyze large sets of data in a usable format from the cloud or legacy systems in a cost-effective manner.
In traditional local relational databases, online analytical processing (OLAP) cubes and data marts may be used to help end users report and analyze large sets of data. This can be effective; however, there can be several drawbacks including lack of flexibility in the way that data is stored and the total cost of ownership. Standard reporting requirements can be used to determine the most common data sets that are returned; however, in many cases, the user is performing ad hoc reporting to solve a specific problem or analyze a trend. The costs to implement these solutions, the costs for maintenance, and the costs to make design changes for reporting can be extremely high.
Companies are moving more of their business data to the cloud; however, a large portion of business data remains in local relational databases. Reporting on data located in the cloud as well as data located in local relational databases can be extremely difficult.
Some companies are trying to address these challenges by moving data from local relational databases to the cloud in order to bring all of the business data together in a central location for analysis and reporting purposes. There can be drawbacks to this approach. For example, companies must pay storage fees in order for data to reside in the cloud. Further, when all business data resides in the cloud, the cloud provider controls the speed with which data travels through use of application programming interface (API) calls. In addition, each time a request for data is made, a company may incur an associated charge for the API call to be made.